Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial Vehicles

Netted radar system is used to implement detection of aerial targets to cover high-value military sites. To address the lack of an effective strategy for Unmanned Aerial Vehicles (UAVs) to perform Multi-Target Reconnaissance (MTR) against netted radar system, a reconnaissance pseudo-target is shaped and used to guide the UAV to quickly approach a key reconnaissance area. By grouping radar positions, solving for multiple pseudo-targets, and integrating these pseudo-targets, we ultimately obtain an invisible pseudo-target that spans the entire radar detection range. The use of the pseudo-target reduces the dimensionality of the UAV’s observation state, thereby accelerating algorithm convergence. In addition, to solve the problem of the difficulty in determining the reconnaissance importance weights of multiple radars, a state-reward shaping equation combining the pseudo-target and real targets is designed. Finally, a deep reinforcement learning framework based on partially observable Markov processes is built, while a modular scenario pre-training method is used to improve the convergence of the policy network in the case of sparsely distributed high-quality samples. The experimental results show that the trained UAVs can successfully perform reconnaissance on up to six radar targets under physical constraints, and the completion rate for the MTR missions of six radars can reach 73 %.